*
Algorithmic Intelligence* is a collective and pragmatic term for a range of algorithmic methods. 
It contrasts the term *Artificial Intelligence* in two
ways:

    - Firstly, while Artificial Intelligence carries some meaning that there
    is intelligence, but not always something real, Algorithmic Intelligenceis focused on methods that solve a given problem.
    - Secondly, if a method exists to solve a complex looking problem or give
    a good approximation of the solution, then this method belongs to
    Algorithmic Intelligence even if it is too simple to be called Artificial
    Intelligence.

This view on intelligence is not philosophical, as its methods have been identified as key revenue 
drivers in companies like Google, Netflix, UPS, and Walmart.

The topic contrasts the term *Artificial Intelligence*, which has been coined as a "modern myth".

    There is a wide divergence between the nature of machine and human as
    currently understood and as revealed by conceptual analysis. Misconceptions
    about computer potential and misrepresentation of computer power emerge from
    excessive anthropomorphisation of machines - John Kelly

Besides such insights there is a real need for software systems capable of taking actions in 
real-time situations involving sensor inputs, state variables, situation assessments and 
environmental conditions.

*Algorithmic Intelligence* has a core that is methodical. It thus serves as a tribute to the fact 
that computer action for real-life applications refers to an algorithmic, often user-initiated 
process. The learner is an information constructor, assisted by humans to create machine 
representations of objective reality. New information is then linked to prior knowledge. The term 
also includes tackling optimization problems where there is no learning as such, but methods come 
in as a way of dealing with computational hardness, and not bound to evolutionary, neural networks 
and fuzzy logic aspects.

With this workshop we agree that we need an alternative term to stress the focus on the impact 
that well-founded AI algorithms have for the success in practice.

Topics of interest for this workshop include, but are not limited to, research and applications in 
the following:

    - Modeling and Elicitation of Expert Knowledge,
    - Prior Knowledge Integration in Machine Learning Approaches,
    - Intelligent User Interfaces, Ambient Assistance
    - Real-Time AI, Question-Answer Systems,
    - Scalable Data Mining Algorithms,
    - Decision Optimization and Computational Hardness,
    - Intelligence in Games,
    - Image Processing and Video Data Analysis,
    - Statistical Relational Learning and Probabilistic Programming

We expect papers from 5 to 15 pages (LNCS style) for technical content, the presentation of tools 
and ongoing work.

  Important Dates

Paper Submission: (send eMail to one of the co-chairs)

Deadline July 15th, 2011 Midnight Pacific Time

Acceptance/Rejection notification: August 15, 2011

  Participant Interaction

Besides paper presentations - by being a new breed of workshop series - we will have panel 
discussions on limits and possibilities of Algorithmic Intelligence, how to continuously bridge 
the gap between the research fields, and how to push the workshop to some higher level - probably 
publishing - event.

  Workshop Chairs

Carsten Elfers
TZI, Universität Bremen
Email: celfers@tzi.de

Rune Jensen
IT University of Copenhagen
Email: rmj@itu.dk

Hartmut Messerschmidt
TZI, Universität Bremen
Email: hmess@tzi.de

Rasmus Pagh
IT University of Copenhagen
Email: pagh@itu.dk
  Contact Hartmut Messerschmidt for any additional information or visit the workshop homepage at 
http://ai.tzi.de/ .